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Uncovering codon usage patterns during murine embryogenesis and tissue-specific developmental diseases

  • Sarah E. Fumagalli
  • , Sean Smith
  • , Brian Lin
  • , Rahul Paul
  • , Collin Campbell
  • , Luis Santana-Quintero
  • , Anton Golikov
  • , Juan Ibla
  • , Haim Bar
  • , Anton A Komar
  • , Ryan C. Hunt
  • , Michael DiCuccio
  • , Chava Kimchi-Sarfaty
  • Division of Plasma Protein Therapeutics Food and Drug Administration
  • Harvard Medical School
  • University of Connecticut
  • Case Western Reserve University
  • Independent Researcher

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: Mouse models share significant genetic similarities with humans and have expanded our understanding of how embryonic tissue-specific genes influence disease states. By improved analyses of temporal, transcriptional data from these models, we can capture unique tissue codon usage patterns and determine how deviations from these patterns can influence developmental disorders. Methods: We analyzed transcriptomic-weighted data from four mouse strains across three different germ layer tissues (liver, heart, and eye) and through embryonic stages. Applying a multifaceted approach, we calculated relative synonymous codon usage, reduced the dimensionality, and employed machine learning clustering techniques. Results and discussion: These techniques identified relative synonymous codon usage differences/similarities among strains and deviations in codon usage patterns between healthy and disease-linked genes. Original transcriptomic mouse data and RefSeq gene sequences can be found at the associated Mouse Embryo CoCoPUTs (codon and codon pair usage tables) website. Future studies can leverage this resource to uncover further insights into the dynamics of embryonic development and the corresponding codon usage biases that are paramount to understanding disease processes of embryologic origin.
Original languageEnglish
Article number1554773
JournalFrontiers in Genetics
Volume16
DOIs
StatePublished - Jan 1 2025

Keywords

  • clustering methods
  • disease-associated comparison
  • machine learning
  • mouse embryology
  • relative synonymous codon usage
  • tissue-specific
  • transcriptomic-weighted

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